2016

Workshop on the Algorithmic Foundations of Robotics, pages: 22, November 2016 (conference)

Abstract

Stochastic Optimal Control (SOC) typically considers noise only in the process model, i.e. unknown disturbances. However, in many robotic applications that involve interaction with the environment, such as locomotion and manipulation, uncertainty also comes from lack of pre- cise knowledge of the world, which is not an actual disturbance. We de- velop a computationally efficient SOC algorithm, based on risk-sensitive control, that takes into account uncertainty in the measurements. We include the dynamics of an observer in such a way that the control law explicitly depends on the current measurement uncertainty. We show that high measurement uncertainty leads to low impedance behaviors, a result in contrast with the effects of process noise variance that creates stiff behaviors. Simulation results on a simple 2D manipulator show that our controller can create better interaction with the environment under uncertain contact locations than traditional SOC approaches.

Microorganisms move in challenging environments by periodic changes in body shape. In contrast, current artificial microrobots cannot actively deform, exhibiting at best passive bending under external fields. Here, by taking advantage of the wireless, scalable and spatiotemporally selective capabilities that light allows, we show that soft microrobots consisting of photoactive liquid-crystal elastomers can be driven by structured monochromatic light to perform sophisticated biomimetic motions. We realize continuum yet selectively addressable artificial microswimmers that generate travelling-wave motions to self-propel without external forces or torques, as well as microrobots capable of versatile locomotion behaviours on demand. Both theoretical predictions and experimental results confirm that multiple gaits, mimicking either symplectic or antiplectic metachrony of ciliate protozoa, can be achieved with single microswimmers. The principle of using structured light can be extended to other applications that require microscale actuation with sophisticated spatiotemporal coordination for advanced microrobotic technologies.

Many highly dynamic novel mobile robots have been developed being inspired by animals. In this study, we are inspired by a basilisk lizard's ability to run and steer on water surface for a hexapedal robot. The robot has an active tail with a circular plate, which the robot rotates to steer on water. We dynamically modeled the platform and conducted simulations and experiments on steering locomotion with a bang-bang controller. The robot can steer on water by rotating the tail, and the controlled steering locomotion is stable. The dynamic modelling approximates the robot's steering locomotion and the trends of the simulations and experiments are similar, although there are errors between the desired and actual angles. The robot's maneuverability on water can be improved through further research.

This paper introduces a new five-dimensional localization method for an untethered meso-scale magnetic robot, which is manipulated by a computer-controlled electromagnetic system. The developed magnetic localization setup is a two-dimensional array of mono-axial Hall-effect sensors, which measure the perpendicular magnetic fields at their given positions. We introduce two steps for localizing a magnetic robot more accurately. First, the dipole modeled magnetic field of the electromagnet is subtracted from the measured data in order to determine the robot's magnetic field. Secondly, the subtracted magnetic field is twice differentiated in the perpendicular direction of the array, so that the effect of the electromagnetic field in the localization process is minimized. Five variables regarding the position and orientation of the robot are determined by minimizing the error between the measured magnetic field and the modeled magnetic field in an optimization method. The resulting position error is 2.1±0.8 mm and angular error is 6.7±4.3° within the applicable range (5 cm) of magnetic field sensors at 200 Hz. The proposed localization method would be used for the position feedback control of untethered magnetic devices or robots for medical applications in the future.

There is an increasing demand for soft actuators because of their importance in soft robotics, artificial muscles, biomimetic devices, and beyond. However, the development of soft actuators capable of low-voltage operation, powerful actuation, and programmable shape-changing is still challenging. In this work, we propose programmable bilayer actuators that operate based on the large hygroscopic contraction of the copy paper and simultaneously large thermal expansion of the polypropylene film upon increasing the temperature. The electrothermally activated bending actuators can function with low voltages (≤ 8 V), low input electric power per area (P ≤ 0.14 W cm–2), and low temperature changes (≤ 35 °C). They exhibit reversible shape-changing behavior with curvature radii up to 1.07 cm–1 and bending angle of 360°, accompanied by powerful actuation. Besides the electrical activation, they can be powered by humidity or light irradiation. We finally demonstrate the use of our paper actuators as a soft gripper robot and a lightweight paper wing for aerial robotics.

Lab on a Chip, 16(22):4445-4457, Royal Society of Chemistry, October 2016 (article)

Abstract

At the sub-millimeter scale, capillary forces enable robust and reversible adhesion between biological organisms and varied substrates. Current human-engineered mobile untethered micromanipulation systems rely on forces which scale poorly or utilize gripper-part designs that promote manipulation. Capillary forces, alternatively, are dependent upon the surface chemistry (which is scale independent) and contact perimeter, which conforms to the part surface. We report a mobile capillary microgripper that is able to pick and place parts of various materials and geometries, and is thus ideal for microassembly tasks that cannot be accomplished by large tethered manipulators. We achieve the programmable assembly of sub-millimeter parts in an enclosed three-dimensional aqueous environment by creating a capillary bridge between the targeted part and a synthetic, untethered, mobile body. The parts include both hydrophilic and hydrophobic components: hydrogel, kapton, human hair, and biological tissue. The 200 μm untethered system can be controlled with five-degrees-of-freedom and advances progress towards autonomous desktop manufacturing for tissue engineering, complex micromachines, microfluidic devices, and meta-materials.

Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems, IEEE, IROS, October 2016 (conference)

Abstract

One of the central tasks for a household robot is searching for specific objects. It does not only require localizing the target object but also identifying promising search locations in the scene if the target is not immediately visible. As computation time and hardware resources are usually limited in robotics, it is desirable to avoid expensive visual processing steps that are exhaustively applied over the entire image. The human visual system can quickly select those image locations that have to be processed in detail for a given task. This allows us to cope with huge amounts of information and to efficiently deploy the limited capacities of our visual system. In this paper, we therefore propose to use human fixation data to train a top-down saliency model that predicts relevant image locations when searching for specific objects. We show that the learned model can successfully prune bounding box proposals without rejecting the ground truth object locations. In this aspect, the proposed model outperforms a model that is trained only on the ground truth segmentations of the target object instead of fixation data.

In International Conference on Intelligent Robots and Systems (IROS) 2016, IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2016 (inproceedings)

Abstract

Binary descriptors allow fast detection and matching algorithms in computer vision problems. Though binary descriptors can be computed at almost two orders of magnitude faster than traditional gradient based descriptors, they suffer from poor matching accuracy in challenging conditions. In this paper we propose three improvements for binary descriptors in their computation and matching that enhance their performance in comparison to traditional binary and non-binary descriptors without compromising their speed. This is achieved by learning some weights and threshold parameters that allow customized matching under some variations such as lighting and viewpoint. Our suggested improvements can be easily applied to any binary descriptor. We demonstrate our approach on the ORB (Oriented FAST and Rotated BRIEF) descriptor and compare its performance with the traditional ORB and SIFT descriptors on a wide variety of datasets. In all instances, our enhancements outperform standard ORB and is comparable to SIFT.

We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show that 2D joints alone carry a surprising amount of information about body shape. The problem is challenging because of the complexity of the human body, articulation, occlusion, clothing, lighting, and the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D body joint locations. We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints. We do so by minimizing an objective function that penalizes the error between the projected 3D model joints and detected 2D joints. Because SMPL captures correlations in human shape across the population, we are able to robustly fit it to very little data. We further leverage the 3D model to prevent solutions that cause interpenetration. We evaluate our method, SMPLify, on the Leeds Sports, HumanEva, and Human3.6M datasets, showing superior
pose accuracy with respect to the state of the art.

In European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, Springer, 14th European Conference on Computer Vision, October 2016 (inproceedings)

Abstract

In this paper we propose a CNN architecture for semantic image segmentation. We introduce a new “bilateral inception” module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image. The feature spaces for bilateral filtering and other parameters of the module are learned end-to-end using standard backpropagation techniques. The bilateral inception module addresses two issues that arise with general CNN segmentation architectures. First, this module propagates information between (super) pixels while respecting image edges, thus using the structured information of the problem for improved results. Second, the layer recovers a full resolution segmentation result from the lower resolution solution of a CNN. In the experiments, we modify several existing CNN architectures by inserting our inception modules between the last CNN (1 × 1 convolution) layers. Empirical results on three different datasets show reliable improvements not only in comparison to the baseline networks, but also in comparison to several dense-pixel prediction techniques such as CRFs, while being competitive in time.

The caffe framework is one of the leading deep learning toolboxes in the machine learning and computer vision community. While it offers efficiency and configurability, it falls short of a full interface to Python. With increasingly involved procedures for training deep networks and reaching depths of hundreds of layers, creating configuration files and keeping them consistent becomes an error prone process.
We introduce the barrista framework, offering full, pythonic control over caffe. It separates responsibilities and offers code to solve frequently occurring tasks for pre-processing, training and model inspection. It is compatible to all caffe versions since mid 2015 and can import and export .prototxt files.
Examples are included, e.g., a deep residual network implemented in only 172 lines (for arbitrary depths), comparing to 2320 lines in the official implementation for the equivalent model.

On page 2325, Ajay Vikram Singh and Metin Sitti propose a facile surface patterning technique and a specific, strong biotin–streptavidin bonding of bacteria on patterned surfaces to fabricate Janus particles that are propelled by the attached bacteria. Such bacteria-driven Janus microswimmers could be used for future medicine in targeted drug delivery and environmental remediation.

Immobilization of colloidal assemblies onto solid supports via a fast UV-triggered click-reaction is achieved. Transient assemblies of microparticles and colloidal materials can be captured and transferred to solid supports. The technique does not require complex reaction conditions, and is compatible with a variety of particle assembly methods.

We demonstrate that chiral magnesium nanoparticles show remarkable plasmonic extinction- and chiroptical-effects in the ultraviolet region. The Mg nanohelices possess an enhanced local surface plasmon resonance (LSPR) sensitivity due to the strong dispersion of most substances in the UV region.

Information-theoretic principles for learning and acting have been proposed to solve particular classes of Markov Decision Problems. Mathematically, such approaches are governed by a variational free energy principle and allow solving MDP planning problems with information-processing constraints expressed in terms of a Kullback-Leibler divergence with respect to a reference distribution. Here we consider a generalization of such MDP planners by taking model uncertainty into account. As model uncertainty can also be formalized as an information-processing constraint, we can derive a unified solution from a single generalized variational principle. We provide a generalized value iteration scheme together with a convergence proof. As limit cases, this generalized scheme includes standard value iteration with a known model, Bayesian MDP planning, and robust planning. We demonstrate the benefits of this approach in a grid world simulation.

Holographic techniques are fundamental to applications such as volumetric displays(1), high-density data storage and optical tweezers that require spatial control of intricate optical(2) or acoustic fields(3,4) within a three-dimensional volume. The basis of holography is spatial storage of the phase and/or amplitude profile of the desired wavefront(5,6) in a manner that allows that wavefront to be reconstructed by interference when the hologram is illuminated with a suitable coherent source. Modern computer-generated holography(7) skips the process of recording a hologram from a physical scene, and instead calculates the required phase profile before rendering it for reconstruction. In ultrasound applications, the phase profile is typically generated by discrete and independently driven ultrasound sources(3,4,8-12); however, these can only be used in small numbers, which limits the complexity or degrees of freedom that can be attained in the wavefront. Here we introduce monolithic acoustic holograms, which can reconstruct diffraction-limited acoustic pressure fields and thus arbitrary ultrasound beams. We use rapid fabrication to craft the holograms and achieve reconstruction degrees of freedom two orders of magnitude higher than commercial phased array sources. The technique is inexpensive, appropriate for both transmission and reflection elements, and scales well to higher information content, larger aperture size and higher power. The complex three-dimensional pressure and phase distributions produced by these acoustic holograms allow us to demonstrate new approaches to controlled ultrasonic manipulation of solids in water, and of liquids and solids in air. We expect that acoustic holograms will enable new capabilities in beam-steering and the contactless transfer of power, improve medical imaging, and drive new applications of ultrasound.

Direct detection of molecular chirality is practically impossible by methods of standard nuclear magnetic resonance (NMR) that is based on interactions involving magnetic-dipole and magnetic-field operators. However, theoretical studies provide a possible direct probe of chirality by exploiting an enantiomer selective additional coupling involving magnetic-dipole, magnetic-field, and electric field operators. This offers a way for direct experimental detection of chirality by nuclear magneto-electric resonance (NMER). This method uses both resonant magnetic and electric radiofrequency (RF) fields. The weakness of the chiral interaction though requires a large electric RF field and a small transverse RF magnetic field over the sample volume, which is a non-trivial constraint. In this study, we present a detailed study of the NMER concept and a possible experimental realization based on a loop-gap resonator. For this original device, the basic principle and numerical studies as well as fabrication and measurements of the frequency dependence of the scattering parameter are reported. By simulating the NMER spin dynamics for our device and taking the F-19 NMER signal of enantiomer-pure 1,1,1-trifluoropropan-2-ol, we predict a chirality induced NMER signal that accounts for 1%-5% of the standard achiral NMR signal. Published by AIP Publishing.

The adhesive system of geckos has inspired hundreds of synthetic adhesives. While this system has been used relentlessly as a source of inspiration, less work has been done in reverse, where synthetics are used to test questions and hypotheses about the natural system. Here we take such an approach. We tested shear adhesion of a mushroom-tipped synthetic gecko adhesive under conditions that produced perplexing results in the natural adhesive system. Synthetic samples were tested at two temperatures (12 °C and 32 °C) and four different humidity levels (30%, 55%, 70%, and 80% RH). Surprisingly, adhesive performance of the synthetic samples matched that of living geckos, suggesting that uncontrolled parameters in the natural system, such as surface chemistry and material changes, may not be as influential in whole-animal performance as previously thought. There was one difference, however, when comparing natural and synthetic adhesive performance. At 12 °C and 80% RH, adhesion of the synthetic structures was lower than expected based on the natural system’s performance. Our approach highlights a unique opportunity for both biologists and material scientists, where new questions and hypotheses can be fueled by joint comparisons of the natural and synthetic systems, ultimately improving knowledge of both.

Sperm-shaped microrobots are controlled under the influence of weak oscillating magnetic fields (milliTesla range) to selectively target cell mockups (i.e., gas bubbles with average diameter of 200 μm). The sperm-shaped microrobots are fabricated by electrospinning using a solution of polystyrene, dimethylformamide, and iron oxide nanoparticles. These nanoparticles are concentrated within the head of the microrobot, and hence enable directional control along external magnetic fields. The magnetic dipole moment of the microrobot is characterized (using the flip-time technique) to be 1.4×10-11 A.m2, at magnetic field of 28 mT. In addition, the morphology of the microrobot is characterized using Scanning Electron Microscopy images. The characterized parameters and morphology are used in the simulation of the locomotion mechanism of the microrobot to prove that its motion depends on breaking the time-reversal symmetry, rather than pulling with the magnetic field gradient. We experimentally demonstrate that the microrobot can controllably follow S-shaped, U-shaped, and square paths, and selectively target the cell mockups using image guidance and under the influence of the oscillating magnetic fields.

In 2016 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), pages: 1-5, July 2016 (inproceedings)

Abstract

One of the main challenges in the development of microrobots, i.e. robots at the sub-millimeter scale, is the difficulty of adopting traditional solutions for power, control and, especially, actuation. As a result, most current microrobots are directly manipulated by external fields, and possess only a few passive degrees of freedom (DOFs). We have reported a strategy that enables embodiment, remote powering and control of a large number of DOFs in mobile soft microrobots. These consist of photo-responsive materials, such that the actuation of their soft continuous body can be selectively and dynamically controlled by structured light fields. Here we use finite-element modelling to evaluate the effective number of DOFs that are addressable in our microrobots. We also demonstrate that by this flexible approach different actuation patterns can be obtained, and thus different locomotion performances can be achieved within the very same microrobot. The reported results confirm the versatility of the proposed approach, which allows for easy application-specific optimization and online reconfiguration of the microrobot's behavior. Such versatility will enable advanced applications of robotics and automation at the micro scale.

In this paper, we present the analysis of the torque transmitted to a tilted permanent magnet that is to be embedded in a capsule robot to achieve targeted drug delivery. This analysis is carried out by using an analytical model and experimental results for a small cubic permanent magnet that is driven by an external magnetic system made of an array of arc-shaped permanent magnets (ASMs). Our experimental results, which are in agreement with the analytical results, show that the cubic permanent magnet can safely be actuated for inclinations lower than 75° without having to make positional adjustments in the external magnetic system. We have found that with further inclinations, the cubic permanent magnet to be embedded in a drug delivery mechanism may stall. When it stalls, the external magnetic system's position and orientation would have to be adjusted to actuate the cubic permanent magnet and the drug release mechanism. This analysis of the transmitted torque is helpful for the development of real-time control strategies for magnetically articulated devices.

The purpose of this thesis is the study of non-parametric models for structured data and their fields of application in computer vision. We aim at the development of context-sensitive architectures which are both expressive and efficient. Our focus is on directed graphical models, in particular Bayesian networks, where we combine the flexibility of non-parametric local distributions with the efficiency of a global topology with bounded treewidth. A bound on the treewidth is obtained by either constraining the maximum indegree of the underlying graph structure or by introducing determinism. The non-parametric distributions in the nodes of the graph are given by decision trees or kernel density estimators.
The information flow implied by specific network topologies, especially the resultant (conditional) independencies, allows for a natural integration and control of contextual information. We distinguish between three different types of context: static, dynamic, and semantic. In four different approaches we propose models which exhibit varying combinations of these contextual properties and allow modeling of structured data in space, time, and hierarchies derived thereof. The generative character of the presented models enables a direct synthesis of plausible hypotheses.
Extensive experiments validate the developed models in two application scenarios which are of particular interest in computer vision: human bodies and natural scenes. In the practical sections of this work we discuss both areas from different angles and show applications of our models to human pose, motion, and segmentation as well as object categorization and localization. Here, we benefit from the availability of modern datasets of unprecedented size and diversity. Comparisons to traditional approaches and state-of-the-art research on the basis of well-established evaluation criteria allows the objective assessment of our contributions.

We investigate the microswimming behaviour of robotic sperms in viscous fluids. These robotic sperms are fabricated from polystyrene dissolved in dimethyl formamide and iron-oxide nanoparticles. This composition allows the nanoparticles to be concentrated within the bead of the robotic sperm and provide magnetic dipole, whereas the flexibility of the ultra-thin tail enables flagellated locomotion using magnetic fields in millitesla range. We show that these robotic sperms have similar morphology and swimming behaviour to those of sperm cells. Moreover, we show experimentally that our robotic sperms swim controllably at an average speed of approximately one body length per second (around 125 μm s−1), and they are relatively faster than the microswimmers that depend on planar wave propulsion in low-Reynolds number fluids.

Nanoplasmonic systems are valued for their strong optical response and their small size. Most plasmonic sensors and systems to date have been rigid and passive. However, rendering these structures dynamic opens new possibilities for applications. Here we demonstrate that dynamic plasmonic nanoparticles can be used as mechanical sensors to selectively probe the rheological properties of a fluid in situ at the nanoscale and in microscopic volumes. We fabricate chiral magneto-plasmonic nanocolloids that can be actuated by an external magnetic field, which in turn allows for the direct and fast modulation of their distinct optical response. The method is robust and allows nanorheological measurements with a mechanical sensitivity of similar to 0.1 cP, even in strongly absorbing fluids with an optical density of up to OD similar to 3 (similar to 0.1% light transmittance) and in the presence of scatterers (e.g., 50% v/v red blood cells).

In 2016 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), pages: 1-5, July 2016 (inproceedings)

Abstract

Miniaturized actuators are a key element for the manipulation and automation at small scales. Here, we propose a new miniaturized actuator, which consists of an array of micro gas bubbles immersed in a fluid. Under ultrasonic excitation, the oscillation of micro gas bubbles results in acoustic streaming and provides a propulsive force that drives the actuator. The actuator was fabricated by lithography and fluidic streaming was observed under ultrasound excitation. Theoretical modelling and numerical simulations were carried out to show that lowing the surface tension results in a larger amplitude of the bubble oscillation, and thus leads to a higher propulsive force. Experimental results also demonstrate that the propulsive force increases 3.5 times when the surface tension is lowered by adding a surfactant. An actuator with a 4×4 mm 2 surface area provides a driving force of about 0.46 mN, suggesting that it is possible to be used as a wireless actuator for small-scale robots and medical instruments.

In Proceedings of the American Control Conference (ACC), Boston, MA, USA, July 2016 (inproceedings)

Abstract

Most widely-used state estimation algorithms, such as the Extended Kalman Filter and the Unscented Kalman Filter, belong to the family of Gaussian Filters (GF). Unfortunately, GFs fail if the measurement process is modelled by a fat-tailed distribution. This is a severe limitation, because thin-tailed measurement models, such as the analytically-convenient and therefore widely-used Gaussian distribution, are sensitive to outliers. In this paper, we show that mapping the measurements into a specific feature space enables any existing GF algorithm to work with fat-tailed measurement models. We find a feature function which is optimal under certain conditions. Simulation results show that the proposed method allows for robust filtering in both linear and nonlinear systems with measurements contaminated by fat-tailed noise.

Realistic, metrically accurate, 3D human avatars are useful for games, shopping, virtual reality, and health applications. Such avatars are not in wide use because solutions for creating them from high-end scanners, low-cost range cameras, and tailoring measurements all have limitations. Here we propose a simple solution and show that it is surprisingly accurate. We use crowdsourcing to generate attribute ratings of 3D body shapes corresponding to standard linguistic descriptions of 3D shape. We then learn a linear function relating these ratings to 3D human shape parameters. Given an image of a new body, we again turn to the crowd for ratings of the body shape. The collection of linguistic ratings of a photograph provides remarkably strong constraints on the metric 3D shape. We call the process crowdshaping and show that our Body Talk system produces shapes that are perceptually indistinguishable from bodies created from high-resolution scans and that the metric accuracy is sufficient for many tasks. This makes body “scanning” practical without a scanner, opening up new applications including database search, visualization, and extracting avatars from books.

Existing remotely actuated magnetic microrobots exhibit a maximum of only five-degree-of-freedom (DOF) actuation, as creation of a driving torque about the microrobot magnetization axis is not achievable. This lack of full orientation control limits the effectiveness of existing microrobots for precision tasks of object manipulation and orientation for advanced medical, biological and micromanufacturing applications. This paper presents a magnetic actuation method that allows remotely powered microrobots to achieve full six-DOF actuation by considering the case of a non-uniform magnetization profile within the microrobot body. This non-uniform magnetization allows for additional rigid-body torques to be induced from magnetic forces via a moment arm. A general analytical model presents the working principle for continuous and discrete magnetization profiles, which is applied to permanent or non-permanent (soft) magnet bodies. Several discrete-magnetization designs are also presented which possess reduced coupling between magnetic forces and induced rigid-body torques. Design guidelines are introduced which can be followed to ensure that a magnetic microrobot design is capable of six-DOF actuation. A simple permanent-magnet prototype is fabricated and used to quantitatively demonstrate the accuracy of the analytical model in a constrained-DOF environment and qualitatively for free motion in a viscous liquid three-dimensional environment. Results show that desired forces and torques can be created with high precision and limited parasitic actuation, allowing for full six-DOF actuation using limited feedback control

This paper considers the task of articulated human pose estimation of multiple people in real-world images. We propose an approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other.
This joint formulation is in contrast to previous strategies, that address the problem by first detecting people and subsequently estimating their body pose. We propose a partitioning and labeling formulation of a set of body-part hypotheses generated with CNN-based part detectors. Our formulation, an instance of an integer linear program, implicitly performs non-maximum suppression on the set of part candidates and groups them to form configurations of body parts respecting geometric and appearance constraints. Experiments on four different datasets demonstrate state-of-the-art results for both single person and multi person pose estimation.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems